Optimization of iterative reconstruction parameters with

Optimization of iterative reconstructionparameters with attenuation correction,scatter correction and resolution recovery inmyocardial perfusion SPECT/CT

著者 Okuda Koichi, Nakajima Kenichi, Yamada Masato,Wakabayashi Hiroshi, Ichikawa Hajime, AraiHiroyuki, Matsuo Shinro, Taki Junichi,Hashimoto Mitsumasa, Kinuya Seigo

journal orpublication title

Annals of Nuclear Medicine

volume 28number 1page range 60-68year 2014-01-01URL http://hdl.handle.net/2297/36500

doi: 10.1007/s12149-013-0785-6

1

Optimization of iterative reconstruction parameters in MPI

[Original article]

Optimization of iterative reconstruction parameters with attenuation correction,

scatter correction and resolution recovery in myocardial perfusion SPECT/CT

Abstract

Objective: The aim of this study was to characterize the optimal reconstruction parameters for

ordered-subset expectation maximization (OSEM) with attenuation correction, scatter correction

and depth-dependent resolution recovery (OSEMACSCRR). We assessed the optimal parameters for

OSEMACSCRR in an anthropomorphic torso phantom study, and evaluated the validity of the

reconstruction parameters in the groups of normal volunteers and patients with abnormal perfusion.

Methods: Images of the anthropomorphic torso phantom, 9 normal volunteers and 7 patients

undergoing myocardial perfusion SPECT were acquired with a SPECT/CT scanner. SPECT data

comprised a 64 × 64 matrix with an acquisition pixel size of 6.6 mm. A normalized mean square

error (NMSE) of the phantom image was calculated to determine both optimal OSEM update and a

full width at half maximum (FWHM) of Gaussian filter. We validated the myocardial count,

contrast and noise characteristic for clinical subjects derived from OSEMACSCRR processing. OSEM

with depth-dependent resolution recovery (OSEMRR) and filtered back projection (FBP) were

simultaneously performed to compare OSEMACSCRR.

Results: The combination of OSEMACSCRR with 90-120 OSEM update and Gaussian filter with

13.2-14.85 mm FWHM yielded low NMSE value in the phantom study. When we used

OSEMACSCRR with 120 updates and Gaussian filter with 13.2 mm FWHM in the normal volunteers,

myocardial contrast showed significantly higher value than that derived from 120 updates and 14.85

mm FWHM. OSEMACSCRR with the combination of 90-120 OSEM update and 14.85 mm FWHM

produced lowest % root mean square (RMS) noise. Regarding the defect contrast of patients with

abnormal perfusion, OSEMACSCRR with the combination of 90-120 OSEM update and 13.2 mm

FWHM produced significantly higher value than that derived from 90-120 OSEM update and 14.85

mm FWHM. OSEMACSCRR was superior to FBP for the % RMS noise (8.52 ± 1.08 vs. 9.55 ± 1.71,

p = 0.02) and defect contrast (0.368 ± 0.061 vs. 0.327 ± 0.052, p = 0.01) , respectively.

Conclusions: Clinically optimized the number of OSEM updates and FWHM of Gaussian filter

were (1) 120 updates and 13.2 mm, and (2) 90-120 update and 14.85 mm on the OSEMACSCRR

processing, respectively. Further assessment may be required to determine the optimal iterative

reconstruction parameters in a larger patient population.

Key Words: myocardial perfusion SPECT, ordered-subset expectation maximization, attenuation

correction, scatter correction, depth-dependent resolution recovery.

2


Introduction

Iterative reconstruction (1), such as ordered-subset expectation maximization (OSEM) (2),

is an indispensable technology for the corrections of depth dependent blurring, photon attenuation

and scatter in the field of nuclear medicine. As for myocardial perfusion single-photon emission

computed tomography (SPECT) imaging (MPI), iterative reconstruction improves the

signal-to-noise ratio of myocardial perfusion counts and the myocardial uptake overlapped with the

hepatic uptake. Attenuation correction (AC)，scatter correction (SC) and resolution recovery (RR)

algorithm can be incorporated into the iterative reconstruction processing, which is suggested for

the cardiac SPECT in the European Association of Nuclear Medicine / European Society of

Cardiology guidelines (3, 4).

The latest iterative reconstruction technologies (5, 6) are commercially available as Flash

3D (Siemens Medical Solutions, Erlangen, Germany) (7-9), Astonish (Philips Medical Systems,

Milpitas, CA, USA) (10-12), Evolution for Cardiac (GE Healthcare, Waukesha, WI, USA) (13) and

wide beam reconstruction (UltraSPECT, Haifa, Israel) (13, 14). Since noise reduction algorithm is

incorporated into the latest iterative reconstruction processing as well as AC, SC and RR, half time

cardiac SPECT imaging became feasible (11, 13-16). Consequently, the image quality for the

half-time SPECT imaging is equivalent to that for the conventional full-time SPECT imaging in

clinical studies (11, 13, 16).

However, optimal reconstruction parameters have not been clearly described in the latest

cardiac OSEM processing with AC, SC and RR algorithm (OSEMACSCRR). In addition, an optimal

cutoff value for filter processing also has not been characterized. The goal of this study was to

determine the optimal OSEM reconstruction parameters on Flash 3D processing. We initially

determined the optimal parameters in an anthropomorphic torso phantom study. Consequently, we

applied the optimized OSEM parameters to a clinical MPI study, and evaluated myocardial count,

contrast and noise characteristic in the groups of normal volunteers and patients with abnormal

perfusion.

Material and Methods

Anthropomorphic torso phantom

We utilized an anthropomorphic torso phantom configured with the cardiac, pulmonary and

hepatic components (Kyoto Kagaku, Kyoto, Japan). The left ventricular (LV) myocardium and liver

were filled with 199 and 24 MBq of Tc-99m pertechnetate, respectively. The left and right

ventricular cavities were filled with water. Four plastic circular defects with 20 mm diameter were

placed in the mid anterior, lateral, inferior and septal walls.

3


Study population

The population included 9 normal volunteers (6 males, mean age: 31 ± 10) and 7 patients

with abnormal perfusion (4 males, mean age: 80 ± 6). There was no statistically difference between

the body heights of the two groups as well as body weights (158 ± 11 cm vs. 165 ± 7 cm, 54 ± 11 kg

vs. 60 ± 10 kg, respectively). The averages of ejection fraction (EF), end-systolic volume (ESV) and

end-diastolic volume (EDV) were 65 ± 6 %, 29 ± 15 ml and 78 ± 25 ml for the normal volunteers,

and 55 ± 13 %, 53 ± 50 ml and 108 ± 77 ml for the patients with abnormal perfusion, respectively.

Averaged summed rest score (SRS) was 16.7 ± 7.1 for the patients with abnormal perfusion. The

institutional ethical committee approved the normal volunteer study, and all volunteers gave

informed consent. We retrospectively enrolled the patients with abnormal perfusion, who underwent

rest gated MPI. All phantom and clinical researches were performed at Kanazawa University

Hospital.

Image acquisition and data processing

SPECT acquisition was performed with a dual-head gamma camera (Symbia T6 hybrid

SPECT/CT scanner, Siemens Japan, Tokyo, Japan) equipped with a low-energy high-resolution

collimator. A photopeak window of 99m

Tc was set as a 15 % energy window centered at 140 keV,

and a low sub window for SC was set as a 7 % of photopeak window (120 – 129 KeV). The

acquisition pixel size was a 6.6 mm for a 64 × 64 matrix. In the phantom study, we acquired two

axial images, which were reconstructed by 60 projection data, to calculate the normalized mean

square error (NMSE) (17, 18). In the clinical study, we performed rest gated-99m

Tc-sestamibi

(MIBI) MPI with 16 frames per cardiac cycle on the hybrid SPECT/CT scanner. MPI was

performed with the 360-degree circular acquisition with 60 projections at 40 minutes after injection

of 99m

Tc MIBI of 300-370 MBq. An acquisition time was set as 35 seconds per projection. We

acquired a low-dose computed tomography (CT) image for AC using a 6-detector row CT on the

SPECT/CT scanner. Tube voltage and effective mAs for AC CT were 130 kV and 20 mAs,

respectively. The axial image was reconstructed with a thickness of 5.0 mm.

Data analysis

We used three reconstruction processings: OSEMACSCRR, OSEM with RR (OSEMRR) and

filtered back projection (FBP) in the phantom and clinical studies. AC SC and RR algorithm was

not incorporated into the conventional FBP processing. When the number of subsets was constantly

set as 15, the number of iterations was set from 1 to 30 (the range of OSEM update: 15 to 450). We

utilized Gaussian post-filter for both OSEMACSCRR and OSEMRR, and Butterworth filter for FBP.

The full width at half maximum (FWHM) of Gaussian filter was set from 6.6 mm to 14.85 mm. The

4


cutoff frequency for Butterworth filter was set as 0.68 Nyquist. All the OSEMACSCRR, OSEMRR and

FBP were processed using the e.soft version 8.1 (Siemens Japan, Tokyo, Japan).

When we calculated NMSE value for the phantom image, the equation of NMSE

calculation was as follows:

𝑁𝑀𝑆𝐸 =∑∑∑((𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒(𝑖, 𝑗, 𝑘) − 𝑇𝑒𝑠𝑡(𝑖, 𝑗, 𝑘))2

𝑧

𝑘=1

𝑦

𝑗=1

𝑥

𝑖=1

∑∑∑(𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒(𝑖, 𝑗, 𝑘))2,

𝑧

𝑘=1

𝑦

𝑗=1

𝑥

𝑖=1

⁄

where the Reference (i, j, k) represented a predicted pixel value on a reference standard image. The

Test (i, j, k) represented an actual pixel value on a test image. An acquisition time for the reference

standard imaging was set at ten times as much as that for the test imaging. Summed acquisition

counts for the reference and test images were 18.1 and 1.8 million, respectively. The projection data

for the reference and test imaging were reconstructed using OSEMACSCRR with the combinations of

OSEM update (15 to 450) and FWHM of Gaussian filter (6.6 to 14.85 mm).

Averaged LV count was calculated using the circumferential profile analysis in the apical,

mid and basal short-axis slices. % root mean square (RMS) noise for the LV counts was defined as

the equation of (standard deviation / mean) × 100. Myocardial contrast was also defined as the

equation of (maximum LV count – background) / (maximum LV count + background). An averaged

background count was calculated using square region of interest (3 × 3 pixels) at the center of the

ventricular cavity on the mid and basal short-axis slices. In the clinical assessment of patients with

abnormal perfusion, we defined the region of perfusion defect as a background count, and

calculated the defect contrast.

SRS, EF, ESV and EDV were automatically calculated with quantitative perfusion SPECT

(QPS) and quantitative gated SPECT (QGS) version 2008.1 (Cedars-Sinai Medical Center, Los

Angeles, CA, USA). FBP processing was used to reconstruct the image for the calculation of EF

and left ventricular volume. SRS was calculated using 17 segment model for LV segmentation.

When we calculated SRS in the patients with abnormal perfusion, sex-specific attenuation corrected

normal databases were utilized.

Statistical analysis

All continuous values were expressed as mean ± standard deviation. A paired t test was

used to analyze the differences in paired continuous data. A one-way repeated measure analysis of

variance was used to analyze the parametric data, and the pairwise comparison was performed using

the Bonferroni correction for p values. The Friedman test was also used to analyze the

non-parametric data. All statistical tests were two-tailed, and a p value of less than 0.05 was

considered significant. These analyses were performed by using MedCalc software version 11.2.1.0

(Mariakerte, Belgium).

5


Results

Figure 1 shows the vertical long-axis and horizontal long-axis and short-axis displays of

the reconstructed phantom image derived from OSEMACSCRR, OSEMRR and FBP. OSEMACSCRR

showed better uniformity of phantom activity concentration and myocardial delineation in

comparison with OSEMRR and FBP. Figure 2 shows NMSE value for the anthropomorphic phantom

image derived from OSEMACSCRR. When we used Gaussian filter with 13.20 mm FWHM, NMSE

value reached a plateau at 90 OSEM updates, and lower NMSE value was observed at 120 OSEM

updates. In addition, there was no significant difference between NMSE values derived from

Gaussian filters with 13.2 and 14.85 mm FWHMs. Consequently we focused on myocardial

contrast, % RMS noise and defect contrast derived from OSEMACSCRR with 90 and 120 updates in

the following clinical study, and also focused on those from Gaussian filter with 13.2 and 14.85

FWHMs.

Figure 1. Vertical long-axis, horizontal long-axis and (apical, mid and basal) short-axis displays of

anthropomorphic phantom derived from OSEMACSCRR (A), OSEMRR (B) and FBP (C). 120 OSEM

updates and Gaussian filter with 13.2 mm FWHM were used for OSEMACSCRR and OSEMRR.

6


Figure 2. NMSE value derived from OSEMACSCRR on the anthropomorphic torso phantom study.

The FWHM of Gaussian filter was set from 6.60 mm to 14.85 mm.

Figure 3a shows the relationship between OSEM update and the average of normalized

myocardial count in the normal volunteer group. When we used Gaussian filters with 13.2 and

14.85 mm FWHMs, myocardial counts reached plateaus at 120 OSEM updates. Figure 3b shows

the relationship between OSEM update and contrast between normal myocardial uptake and

background. There was significant difference between the contrasts derived from Gaussian filters

with 13.2 and 14.85 mm FWHMs. There was also significant difference between the contrasts

derived from 90 and 120 OSEM updates. OSEMACSCRR with 120 OSEM updates and Gaussian filter

with 13.2 mm FWHM showed the highest contrast in the combination of 90-120 OSEM update and

Gaussian filter with 13.2-14.85 mm FWHM. Figure 3c shows the relationship between OSEM

update and % RMS noise. Although significant difference was observed in the % RMS noises

derived from Gaussian filters with 13.2 and 14.85 mm FWHMs, no significant difference was

observed between 90 and 120 OSEM updates. Figure 4 shows the relationship between OSEM

update and defect contrast. When we used Gaussian filters with 13.2 and 14.85 mm FWHMs, defect

contrasts reached plateaus at 90 OSEM updates. There was significant difference between the defect

contrasts derived from the Gaussian filters with 13.2 and 14.85 mm FWHMs.

7


Figure 3. Relationships between

OSEM update and normalized

count (a), contrast (b) and noise

characteristic (%RMS noise) (c)

of the left ventricular uptake

derived from OSEMACSCRR in the

normal volunteer group.

Horizontal dotted line shows the

contrast and ％ RMS noise

derived from FBP in Figures 3b

and 3c.

8


Figure 4. Relationship between OSEM update and defect contrast for patients with abnormal

perfusion. Horizontal dotted line shows the defect contrast derived from FBP.

Figure 5 shows a flow chart for the optimization process of iterative reconstruction

parameters. We experimentally characterized the optimized OSEM update and FWHM of Gaussian

filter as 90-120 and 13.2-14.85 mm in the anthropomorphic torso phantom study, respectively.

Consequently, after we evaluated the contrast for normal uptake, % RMS noise and defect contrast

in the clinical MPI study, the optimized OSEM update and FWHM of Gaussian filter were

characterized as both (1) 90-120 OSEM update and Gaussian filter with 14.85 mm FWHM and (2)

120 OSEM updates and Gaussian filter with 13.2 mm FWHM.

Figure 5. Flow chart for the optimization process of iterative reconstruction parameters.

9


Table 1 shows the comparison between OSEMACSCRR and FBP in the results of contrast for

normal myocardial uptake, % RMS noise and defect contrast. Contrast for normal myocardial

uptake derived from OSEMACSCRR was equivalent to that from FBP. % RMS noises derived from

OSEMACSCRR with 90-120 update and Gaussian filter with 14.85 mm FWHM were better than that

from FBP (8.52 ± 1.08 and 8.45 ± 0.91 vs. 9.55 ± 1.71, p = 0.02, respectively). Defect contrasts

derived from OSEMACSCRR with 90-120 update and Gaussian filter with 13.2 mm FWHM showed

significantly higher values than that from FBP (0.368 ± 0.061 and 0.371 ± 0.061 vs. 0.327 ± 0.052,

p < 0.01).

Table 1. Results of contrast between normal myocardial uptake and background, % RMS noise and defect contrast

derived from OSEMACSCRC and FBP.

FWHM of Gaussian filter

FBP OSEM

update 13.2 mm

p value

(vs. FBP) 14.85 mm

p value

(vs. FBP)

Contrast between normal myocardial uptake and background

90 0.63 ± 0.07 n.s. 0.58 ± 0.07 n.s. 0.60 ± 0.10

120 0.66 ± 0.07 n.s. 0.61 ± 0.08 n.s.

% RMS noise

90 8.88 ± 1.24 n.s. 8.52 ± 1.08 0.02 9.55 ± 1.71

120 8.79 ± 0.98 n.s. 8.45 ± 0.91 0.02

Defect contrast

90 0.368 ± 0.061 0.01 0.352 ± 0.061 n.s. 0.327 ± 0.052

120 0.371 ± 0.061 <0.01 0.354 ± 0.050 n.s.

Figure 6 shows the vertical long-axis, horizontal long-axis and (apical, mid and basal)

short-axis displays of the normal volunteer and patient with abnormal perfusion derived from

OSEMACSCRR, OSEMRR and FBP. When we used OSEMACSCRR with the optimized reconstruction

parameters, myocardial infarction was clearly delineated.

10


Figure 6. Example of vertical long-axis, horizontal long-axis and (apical, mid and basal) short-axis

displays of the normal volunteer (a) and patient with abnormal perfusion (b). 120 OSEM updates

and Gaussian filter with 13.2 mm FWHM were used in the OSEMACSCRR and OSEMRR.

11


Discussion

We characterized the optimal iterative reconstruction parameters for OSEMACSCRR

processing in the phantom and clinical studies. In the phantom study, OSEMACSCRR with 90-120

update and Gaussian filter with 13.2-14.85 mm FWHM produced low NMSE value. We determined

that the clinically optimized iterative reconstruction parameters were both (1) 90 OSEM updates

with 14.85 mm FWHM and (2) 90-120 OSEM update with 13.2–14.85mm FWHM in the groups of

normal volunteers and patients with abnormal perfusion.

The optimizations for OSEM reconstruction parameters have been described in many

studies. The number of updates for OSEM with no compensation was ranged from 8 to 50 in the

bone, gallium and MPI studies (19-22). As for OSEMACSCRR processing in clinical MPI studies, 75

OSEM updates were used (23, 24). Comparing the optimal number of OSEM updates between

OSEMACSCRR and OSEM with no compensation, OSEMACSCRR required more OSEM update than

OSEM with no compensation to reconstruct the projection data. However, Astonish technology

required only 24 OSEM updates in clinical MPI study (12). Moreover, when we used the wide

beam reconstruction technology, suitable number of OSEM updates was automatically determined.

We will need further investigation of the latest OSEMACSCRR processing (18).

The combination of OSEM update and Gaussian filter significantly affected the perfusion

count, contrast and noise characteristic in the phantom and clinical studies. Moreover, there was a

trade-off between the number of OSEM updates and the FWHM of Gaussian filter. For example,

the delineation of normal myocardium derived from OSEMACSCRR with 90 updates and Gaussian

filtering occasionally shows equivalent to that from OSEMACSCRR with 50 updates and no filtering.

However, OSEMACSCRR with 50 updates is not enough to correct a depth-dependent blurring.

Therefore, when the number of OSEM updates has been increased until the measured reconstruction

data matches with the estimated reconstruction data, Gaussian filter should be applied to the

reconstructed image to decrease the statistical noise.

Clinical implication of this study was that we experimentally determined that the optimized

iterative reconstruction parameters were both 90 OSEM updates with 13.2 mm-FWHM Gaussian

filter and 90-120 OSEM update with 13.2-14.85 mm-FWHM Gaussian filter in the clinical MPI

studies. In comparison with FBP processing, % RMS noise and defect delineation were significantly

improved with OSEMACSCRR processing. Whereas, normal myocardial contrast derived from

OSEMACSCRR was equivalent to that from FBP. In the diagnostic performances of clinical MPI using

OSEMACSCRR, OSEMACSCRR was superior to FBP regarding the sensitivity (82.2 ± 2.7 vs. 65.9 ± 4.9,

p < 0.001) and specificity (82.6 ± 3.0 vs. 66.7 ± 4.5, p = 0.001) for the detection of coronary artery

disease (CAD) (24). Pretorius also reported that OSEMACSCRR yielded significantly better detection

of CAD than FBP (23).

Our study has several limitations. We did not describe the optimal number of subsets. In

our preliminary study for the evaluation of subset, when we acquired 60 projection datasets using

12


the NEMA IEC PET phantom, equivalent image quality was visually observed among the OSEM

updates of 15 subsets × 6 iterations, 10 subsets × 9 iterations, 6 subsets × 15 iterations, 5 subsets ×

18 iterations, and 3 subsets × 30 iterations. In the low-count SPECT imaging, optimal number of

OSEM updates may be modified. This is because reconstructed image quality is determined by the

count statistics within the subset. We will need the further investigation for optimal OSEM

reconstruction parameters in the low-count acquisition. Finally, we did not have enough normal

volunteers and patients with abnormal perfusion to evaluate the myocardial count, contrast and

noise characteristic. Further assessment may be needed to confirm this observation in a larger

patient population.

Conclusion

We determined that the optimized OSEM update and FWHM of Gaussian filter were both

(1) 90 updates and 13.2 mm and (2) 90-120 update and 13.2-14.85 mm in the clinical MPI study,

respectively. OSEMACSCRR processing was superior to FBP processing for the noise characteristic

and defect delineation, and equivalent to FBP processing for the myocardial contrast. Further

assessment may be needed to confirm the optimized reconstruction parameters in a larger patient

population.

Acknowledgement : We thank nuclear medicine technologists: Minoru Tobisaka, Prof. Masahisa

Onoguchi, Shigeto Matsuyama, Hiroto Yoneyama, Masakazu Kobayashi, Hironori Kojima,

Takahiro Konishi, Keita Sakuta, Haruka Koshida (Kanazawa University Hospital, Kanazawa,

Japan) for their assistance. We also thank Carole Fujioka, DVM for the grammatical revision. This

work was supported in part by JSPS KAKENHI Grant Number 22591320 and Grant for Promoted

Research from Kanazawa Medical University (S2013-16).

Disclosure Statement : Authors declare no conflict of interest in this study.

13


References

(1) Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE

Trans Med Imaging 1982;1:113-22.

(2) Hudson HM, Larkin RS. Accelerated image reconstruction using ordered subsets of

projection data. IEEE Trans Med Imaging 1994;13:601-9.

(3) Hesse B, Tagil K, Cuocolo A, Anagnostopoulos C, Bardies M, Bax J et al. EANM/ESC

procedural guidelines for myocardial perfusion imaging in nuclear cardiology. Eur J Nucl

Med Mol Imaging 2005;32:855-97.

(4) Hesse B, Lindhardt TB, Acampa W, Anagnostopoulos C, Ballinger J, Bax JJ et al.

EANM/ESC guidelines for radionuclide imaging of cardiac function. Eur J Nucl Med Mol I

2008;35:851-85.

(5) Slomka PJ, Patton JA, Berman DS, Germano G. Advances in technical aspects of

myocardial perfusion SPECT imaging. J Nucl Cardiol 2009;16:255-76.

(6) Zaman MU, Hashmi I, Fatima N. Recent developments and future prospects of SPECT

myocardial perfusion imaging. Ann Nucl Med 2010;24:565-9.

(7) Zeintl J, Vija AH, Yahil A, Hornegger J, Kuwert T. Quantitative accuracy of clinical 99mTc

SPECT/CT using ordered-subset expectation maximization with 3-dimensional resolution

recovery, attenuation, and scatter correction. J Nucl Med 2010;51:921-8.

(8) Ficaro EP, Kritzman JN, Corbett JR. Effect of reconstruction parameters and acquisition

times on myocardial perfusion distribution in normals. J Nucl Cardiol 2008;15:S20.

(9) Ceriani L, Ruberto T, Delaloye AB, Prior JO, Giovanella L. Three-dimensional

ordered-subset expectation maximization iterative protocol for evaluation of left ventricular

volumes and function by quantitative gated SPECT: a dynamic phantom study. J Nucl Med

Technol 2010;38:18-23.

(10) Bateman TM, Heller GV, McGhie AI, Courter SA, Golub RA, Case JA et al. Multicenter

investigation comparing a highly efficient half-time stress-only attenuation correction

approach against standard rest-stress Tc-99m SPECT imaging. J Nucl Cardiol

2009;16:726-35.

(11) Venero CV, Heller GV, Bateman TM, McGhie AI, Ahlberg AW, Katten D et al. A multicenter

evaluation of a new post-processing method with depth-dependent collimator resolution

applied to full-time and half-time acquisitions without and with simultaneously acquired

attenuation correction. J Nucl Cardiol 2009;16:714-25.

(12) Tashiro K, Tomiguchi S, Shiraishi S, Yoshida M, Sakaguchi F, Yamashita Y. Clinical

usefulness of a collimator distance dependent resolution recovery in myocardial perfusion

SPECT: a clinical report from a single institute. Ann Nucl Med 2011;25:133-7.

(13) DePuey EG, Bommireddipalli S, Clark J, Leykekhman A, Thompson LB, Friedman M. A

comparison of the image quality of full-time myocardial perfusion SPECT vs wide beam

14


reconstruction half-time and half-dose SPECT. J Nucl Cardiol 2011;18:273-80.

(14) Borges-Neto S, Pagnanelli RA, Shaw LK, Honeycutt E, Shwartz SC, Adams GL et al.

Clinical results of a novel wide beam reconstruction method for shortening scan time of

Tc-99m cardiac SPECT perfusion studies. J Nucl Cardiol 2007;14:555-65.

(15) Valenta I, Treyer V, Husmann L, Gaemperli O, Schindler MJ, Herzog BA et al. New

reconstruction algorithm allows shortened acquisition time for myocardial perfusion SPECT.

Eur J Nucl Med Mol Imaging 2010;37:750-7.

(16) DePuey EG, Gadiraju R, Clark J, Thompson L, Anstett F, Shwartz SC. Ordered subset

expectation maximization and wide beam reconstruction "half-time" gated myocardial

perfusion SPECT functional imaging: a comparison to "full-time" filtered backprojection. J

Nucl Cardiol 2008;15:547-63.

(17) Penney BC, King MA, Schwinger RB, Baker SP, Stritzke P, Doherty PW. Constrained

least-squares restoration of nuclear medicine images: selecting the coarseness function.

Medical physics 1987;14:849-58.

(18) Onishi H, Motomura N, Fujino K, Natsume T, Haramoto Y. Quantitative performance of

advanced resolution recovery strategies on SPECT images: evaluation with use of digital

phantom models. Radiological physics and technology 2013;6:42-53.

(19) Blocklet D, Seret A, Popa N, Schoutens A. Maximum-likelihood reconstruction with

ordered subsets in bone SPECT. J Nucl Med 1999;40:1978-84.

(20) Case JA, Licho R, King MA, Weaver JP. Bone SPECT of the spine: a comparison of

attenuation correction techniques. J Nucl Med 1999;40:604-13.

(21) Wells RG, King MA, Simkin PH, Judy PF, Brill AB, Gifford HC et al. Comparing filtered

backprojection and ordered-subsets expectation maximization for small-lesion detection and

localization in 67Ga SPECT. J Nucl Med 2000;41:1391-9.

(22) Takahashi Y, Murase K, Higashino H, Sogabe I, Sakamoto K. Receiver operating

characteristic (ROC) analysis of images reconstructed with iterative expectation

maximization algorithms. Ann Nucl Med 2001;15:521-5.

(23) Pretorius PH, King MA, Gifford HC, Dahlberg ST, Spencer F, Simon E et al. Myocardial

perfusion SPECT reconstruction: receiver operating characteristic comparison of CAD

detection accuracy of filtered backprojection reconstruction with all of the clinical imaging

information available to readers and solely stress slices iteratively reconstructed with

combined compensation. J Nucl Cardiol 2005;12:284-93.

(24) Narayanan MV, King MA, Pretorius PH, Dahlberg ST, Spencer F, Simon E et al.

Human-observer receiver-operating-characteristic evaluation of attenuation, scatter, and

resolution compensation strategies for (99m)Tc myocardial perfusion imaging. J Nucl Med

2003;44:1725-34.

Documents

Optimization of iterative reconstruction parameters with